Smart Discharges to Improve Post-discharge Health Outcomes in Children

Sepsis Clinical Trial

Official title:

Smart Discharges to Improve Post-discharge Health Outcomes in Children: A Prospective Before-after Study With Staggered Implementation

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05730452
• Other study ID #: H16-02679
• Secondary ID: F21-05597F17-020
• Status: Recruiting
• Phase: N/A
• Start date: July 16, 2017
• Est. completion date: March 31, 2024

Study information

• Verified date: February 2023
• Source: University of British Columbia
• Contact: Matthew O Wiens, PharmD, PhD
• Phone: 1-604-829-2562
• Email: Matthew.Wiens[@]bcchr.ca
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

In Uganda, about 5% of children discharged after hospitalization for a serious infection will die in the weeks after returning home. Doctors and parents are often unaware of this period of vulnerability and are poorly equipped to identify or handle this critical situation. This project builds on past work to develop and evaluate models and technology to predict, before discharge, an individual child's risk of recurrent illness, as well as to provide additional post-discharge support to at-risk children. This study seeks to evaluate the effect of a novel "Smart Discharges" approach on childhood mortality and health seeking behaviour.

Description:

PURPOSE:

Our purpose is to conduct an interventional cohort study to evaluate the impact of the Smart Discharges approach to discharge care on pediatric post-discharge mortality.

HYPOTHESIS:

Our Smart Discharges approach will improve post-discharge health seeking and reduce post-discharge mortality.

JUSTIFICATION:

With an improved understanding of risk, and an ability to determine risk at the bedside, a Smart Discharge program will ensure optimized resource allocation, focusing on children most in need of limited resources. Such programs in precision public health can not only save lives and resources, but are much more likely to be scalable in economically strained environments.

OBJECTIVES:

The objective of this study is to determine whether a individualized, risk-based approach to improving pediatric discharges will reduce 6-month post-discharge mortality among children admitted with suspected sepsis

The study has two objectives, each corresponding to a phase:

1. Phase I: An observational period with enrolled patients acting as controls. Furthermore, this cohort will be used to update, refine, and validate previously developed models for post-discharge mortality to be used during Phase II.

2. Phase II: An interventional period to evaluate the effectiveness of a Smart Discharge program on mortality and post-discharge health seeking.

RESEARCH DESIGN:

This will be a two-phase study: Phase I is a multi-site prospective observational cohort study, while Phase II is a multi-site prospective interventional cohort study. This prospective study will be conducted from March 2017 to January 2024. The study will enroll 5700 children under five years of age (2700 <6 months of age, 3000 6-60m of age) in Phase I (non-interventional) and an equal number (5700) of children during phase II (interventional phase), for a total of 11,600 children.

STATISTICAL ANALYSIS:

Our prior work has shown that the 6-month post-discharge mortality rate is 5%. Our preliminary work has also suggested that our expected mortality benefit will be between 25% and 30% relative risk reduction. Assuming a relative risk reduction of 22.5%, we would need to enroll 5250 children per arm. We thus will conservatively aim to enroll 5700 per arm to account for losses to follow-up.

All analyses will be conducted using R 4.2.2 (Vienna, Austria; http://www.R-project.org). External model validation will be conducted using the Phase I cohort on the previously developed Smart Discharge Model. The Smart Discharge Model will then be updated to include data from the Phase I cohort. Final prediction models will be developed separately for children <6m of age and for children 6m - 5 years of age, with an area under the ROC curve analysis used to assess the overall performance of the final models. For the final model in older children (6m - 5y), the risk cut-off will be chosen based on the sensitivity and specificity, ensuring a sensitivity of >80% (initial derived model sensitivity was 82%). The final sensitivity and specificity will be reported, along with positive and negative predictive values (based on the overall mortality rate, and the mortality rates of each site). For the model in younger children, the same approach will be used, but ensuring the sensitivity is at least 85%, due to an expected higher mortality rate among younger children. The separation of ages has been determined to be the optimal approach for these models.

To evaluate the effectiveness of the intervention, a cox-proportional hazards regression on the time to post-discharge mortality will be used, including the year of discharge as a covariate to account for potential trends in mortality unrelated to the intervention. Additional potentially confounding variables will be identified based on a combination of pre-existing and expert knowledge, and univariate analysis of potential confounders on outcomes. A final multivariable model will be used to determine the adjusted effect of the Smart Discharge intervention. Interrupted time series, which use segmented regression modeling to determine the effect of the intervention after controlling for pre- and post-intervention time trends, will also be used to account for potential pre-intervention trends in mortality.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 11700
• Est. completion date: March 31, 2024
• Est. primary completion date: January 31, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 0 Days to 59 Months

Eligibility

Inclusion Criteria:

• Children under five years of age

• Admission with a proven or suspected infection

• Provide written informed consent

Exclusion Criteria:

• Refusal to participate

• Previous enrolment in the study

• Outside of hospital catchment

• Language barrier

• Direct admission following birth without having been discharged

Related Conditions & MeSH terms

• Sepsis

Intervention

Behavioral:
• Risk-stratified discharge and post-discharge care
Interventional intensity is based on predicted risk. Predicted risk based on previously developed prediction algorithms. Low risk: receive discharge education and counselling only; Moderate risk: Discharge education and counselling + 1 post-discharge follow-up referral at day 7; High risk: Discharge education and counselling + 3 post-discharge follow-up referrals (D2, D7, D14); Very high risk: Discharge education and counselling + 3 post-discharge follow-up referrals (D2, D7, D14, D28)

Locations

Country	Name	City	State
Canada	BC Children's Hospital Research Institute	Vancouver	British Columbia

Sponsors (6)

Lead Sponsor	Collaborator
University of British Columbia	British Columbia Childrens Hospital Foundation, Grand Challenges Canada, Mbarara University of Science and Technology, Thrasher Research Fund, Walimu

Country where clinical trial is conducted

Canada, 

References & Publications (6)

English LL, Dunsmuir D, Kumbakumba E, Ansermino JM, Larson CP, Lester R, Barigye C, Ndamira A, Kabakyenga J, Wiens MO. The PAediatric Risk Assessment (PARA) Mobile App to Reduce Postdischarge Child Mortality: Design, Usability, and Feasibility for Health Care Workers in Uganda. JMIR Mhealth Uhealth. 2016 Feb 15;4(1):e16. doi: 10.2196/mhealth.5167. — View Citation

Nemetchek B, English L, Kissoon N, Ansermino JM, Moschovis PP, Kabakyenga J, Fowler-Kerry S, Kumbakumba E, Wiens MO. Paediatric postdischarge mortality in developing countries: a systematic review. BMJ Open. 2018 Dec 28;8(12):e023445. doi: 10.1136/bmjopen-2018-023445. — View Citation

Nemetchek BR, Liang LD, Kissoon N, Ansermino JM, Kabakyenga J, Lavoie PM, Fowler-Kerry S, Wiens MO. Predictor variables for post-discharge mortality modelling in infants: a protocol development project. Afr Health Sci. 2018 Dec;18(4):1214-1225. doi: 10.4314/ahs.v18i4.43. — View Citation

Wiens MO, Kissoon N, Kumbakumba E, Singer J, Moschovis PP, Ansermino JM, Ndamira A, Kiwanuka J, Larson CP. Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project. Afr Health Sci. 2016 Mar;16(1):162-9. doi: 10.4314/ahs.v16i1.22. — View Citation

Wiens MO, Kumbakumba E, Larson CP, Ansermino JM, Singer J, Kissoon N, Wong H, Ndamira A, Kabakyenga J, Kiwanuka J, Zhou G. Postdischarge mortality in children with acute infectious diseases: derivation of postdischarge mortality prediction models. BMJ Open. 2015 Nov 25;5(11):e009449. doi: 10.1136/bmjopen-2015-009449. — View Citation

Wiens MO, Kumbakumba E, Larson CP, Moschovis PP, Barigye C, Kabakyenga J, Ndamira A, English L, Kissoon N, Zhou G, Ansermino JM. Scheduled Follow-Up Referrals and Simple Prevention Kits Including Counseling to Improve Post-Discharge Outcomes Among Children in Uganda: A Proof-of-Concept Study. Glob Health Sci Pract. 2016 Sep 29;4(3):422-34. doi: 10.9745/GHSP-D-16-00069. Print 2016 Sep 28. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Post-discharge mortality	Rate of all-cause mortality within 6-months post-discharge	From discharge until 6 months post-discharge
Secondary	Post-discharge re-admission	Rate of all-cause re-admissions within 6-months post-discharge.	From discharge until 6 months post-discharge
Secondary	Post-discharge health seeking	proportion of patients who reported attending a post-discharge referral visit within 6 months post-discharge.; proportion of patients who reported seeking any care within 6 months post-discharge through self referral.	From discharge until 6 months post-discharge